Avicenna: Sequence segments similarity based protein secondary structure prediction method
Başlık çevirisi mevcut değil.
- Tez No: 402736
- Danışmanlar: PROF. MEHMET CAN
- Tez Türü: Doktora
- Konular: Biyomühendislik, Genetik, Bioengineering, Genetics
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2016
- Dil: İngilizce
- Üniversite: International University of Sarajevo
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Genetik ve Biyomühendislik Ana Bilim Dalı
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 150
Özet
Özet yok.
Özet (Çeviri)
Understanding the tertiary structure of proteins can shed light in protein function. Therefore, protein structure determination has become one of the hot topics in structural biology and computational biology. Large-scale sequencing techniques generate a vast amount of protein sequences but number of structurally analyzed proteins still remains much smaller due to difficulties and cost of the experimental processes such as X-ray crystallography and NMR spectroscopy. Prediction of protein secondary structure based on its amino acid sequence is a common technique used to predict protein`s three dimensional structure. In this thesis, the problem of secondary structure prediction is analyzed and several aspects of secondary structure prediction are investigated. An efficient new algorithm“Avicenna”for predicting secondary structure is presented and it`s superiority to the current methods is shown. It is well known that even for two distant proteins, it is possible to find similar segments in amino acid sequences, and the corresponding secondary structure segments are mostly much more similar. Completeness of secondary structures in the current Protein Data Bank (PDB) library is examined with it's around 110.000 proteins whose three dimensional structures are solved for the prediction of the secondary structures of proteins. To address this issue, we employ a comprehensive set of 80,592 non redundant proteins of length larger than 30 residues derived from PDB databank. We mostly find similar segments to a query segment of the amino acid sequence with levels of similarity ranging from full-match to 60% mismatches. Since corresponding secondary structure segments in average have much more higher matches, by the use of consensus of these secondary structure segments, it is possible to predict secondary structure of the query protein with an accuracy of 85% in average. Our results showed that the protein secondary structure prediction problem can in principle be solved based on the current PDB library by developing efficient similarity capturing and information aggregation techniques that can successfully predict secondary structures of unknown proteins.
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